library(outbreaks)
## Warning: package 'outbreaks' was built under R version 3.4.4
data("fluH7N9_china_2013")
class(fluH7N9_china_2013)
## [1] "data.frame"
View(fluH7N9_china_2013)
str(fluH7N9_china_2013)
## 'data.frame': 136 obs. of 8 variables:
## $ case_id : Factor w/ 136 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ date_of_onset : Date, format: "2013-02-19" "2013-02-27" ...
## $ date_of_hospitalisation: Date, format: NA "2013-03-03" ...
## $ date_of_outcome : Date, format: "2013-03-04" "2013-03-10" ...
## $ outcome : Factor w/ 2 levels "Death","Recover": 1 1 1 NA 2 1 1 1 NA 1 ...
## $ gender : Factor w/ 2 levels "f","m": 2 2 1 1 1 1 2 2 2 2 ...
## $ age : Factor w/ 61 levels "?","15","2","21",..: 58 7 11 18 20 9 54 14 39 20 ...
## $ province : Factor w/ 13 levels "Anhui","Beijing",..: 11 11 1 8 8 8 8 13 13 11 ...
head(fluH7N9_china_2013)
## case_id date_of_onset date_of_hospitalisation date_of_outcome outcome
## 1 1 2013-02-19 <NA> 2013-03-04 Death
## 2 2 2013-02-27 2013-03-03 2013-03-10 Death
## 3 3 2013-03-09 2013-03-19 2013-04-09 Death
## 4 4 2013-03-19 2013-03-27 <NA> <NA>
## 5 5 2013-03-19 2013-03-30 2013-05-15 Recover
## 6 6 2013-03-21 2013-03-28 2013-04-26 Death
## gender age province
## 1 m 87 Shanghai
## 2 m 27 Shanghai
## 3 f 35 Anhui
## 4 f 45 Jiangsu
## 5 f 48 Jiangsu
## 6 f 32 Jiangsu
fluH7N9_china_2013$age[fluH7N9_china_2013$age == '?'] <- NA
fluH7N9_china_2013$age <- as.numeric(fluH7N9_china_2013$age)
fluH7N9_china_2013$age
## [1] 58 7 11 18 20 9 54 14 39 20 36 24 39 15 34 51 46 38 31 27 39 56 5
## [24] 36 35 49 23 51 48 53 43 46 37 46 54 40 8 28 38 46 26 25 57 42 28 49
## [47] 44 37 14 10 37 36 35 47 51 45 26 50 22 6 33 40 33 28 4 44 28 29 35
## [70] 30 44 19 41 NA NA 27 3 59 13 46 57 16 14 6 52 26 41 15 26 17 20 38
## [93] 28 11 13 17 47 48 40 30 51 53 40 26 9 12 61 55 35 25 28 41 33 22 14
## [116] 37 48 21 12 33 36 14 26 52 8 52 15 30 41 41 60 51 32 2 34 23
fluH7N9_china_2013$case_id <- paste('case',fluH7N9_china_2013$case_id, sep = '_')
library(tidyr)
## Warning: package 'tidyr' was built under R version 3.4.4
fluH7N9_china_2013_gather <- fluH7N9_china_2013 %>%
gather(Group, Date, date_of_onset:date_of_outcome)
fluH7N9_china_2013[fluH7N9_china_2013$case_id == 'case_1',]
## case_id date_of_onset date_of_hospitalisation date_of_outcome outcome
## 1 case_1 2013-02-19 <NA> 2013-03-04 Death
## gender age province
## 1 m 58 Shanghai
fluH7N9_china_2013_gather[fluH7N9_china_2013$case_id == 'case_1',]
## case_id outcome gender age province Group Date
## 1 case_1 Death m 58 Shanghai date_of_onset 2013-02-19
## 137 case_1 Death m 58 Shanghai date_of_hospitalisation <NA>
## 273 case_1 Death m 58 Shanghai date_of_outcome 2013-03-04
nrow(fluH7N9_china_2013)
## [1] 136
nrow(fluH7N9_china_2013_gather)
## [1] 408
fluH7N9_china_2013_gather$Group <- factor(fluH7N9_china_2013_gather$Group, levels =
c("date_of_onset", "date_of_hospitalisation", "date_of_outcome"))
str(fluH7N9_china_2013_gather)
## 'data.frame': 408 obs. of 7 variables:
## $ case_id : chr "case_1" "case_2" "case_3" "case_4" ...
## $ outcome : Factor w/ 2 levels "Death","Recover": 1 1 1 NA 2 1 1 1 NA 1 ...
## $ gender : Factor w/ 2 levels "f","m": 2 2 1 1 1 1 2 2 2 2 ...
## $ age : num 58 7 11 18 20 9 54 14 39 20 ...
## $ province: Factor w/ 13 levels "Anhui","Beijing",..: 11 11 1 8 8 8 8 13 13 11 ...
## $ Group : Factor w/ 3 levels "date_of_onset",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Date : Date, format: "2013-02-19" "2013-02-27" ...
library(plyr)
## Warning: package 'plyr' was built under R version 3.4.4
fluH7N9_china_2013_gather$Group <-mapvalues(fluH7N9_china_2013_gather$Group, from =
c("date_of_onset", "date_of_hospitalisation", "date_of_outcome"),
to = c("Date of onset", "Date of hospitalisation", "Date of outcome"))
levels(fluH7N9_china_2013_gather$province)
## [1] "Anhui" "Beijing" "Fujian" "Guangdong" "Hebei"
## [6] "Henan" "Hunan" "Jiangsu" "Jiangxi" "Shandong"
## [11] "Shanghai" "Taiwan" "Zhejiang"
fluH7N9_china_2013_gather$province <- mapvalues(fluH7N9_china_2013_gather$province,from =
c("Anhui", "Beijing", "Fujian", "Guangdong", "Hebei", "Henan", "Hunan", "Jiangxi", "Shandong", "Taiwan"),
to = rep("Other", 10))
levels(fluH7N9_china_2013_gather$province)
## [1] "Other" "Jiangsu" "Shanghai" "Zhejiang"
levels(fluH7N9_china_2013_gather$gender) <- c(levels(fluH7N9_china_2013_gather$gender),
"unknown")
fluH7N9_china_2013_gather$gender[is.na(fluH7N9_china_2013_gather$gender)] <- "unknown"
fluH7N9_china_2013_gather$province <- factor(fluH7N9_china_2013_gather$province, levels =
c("Jiangsu", "Shanghai", "Zhejiang", "Other"))
str(fluH7N9_china_2013_gather)
## 'data.frame': 408 obs. of 7 variables:
## $ case_id : chr "case_1" "case_2" "case_3" "case_4" ...
## $ outcome : Factor w/ 2 levels "Death","Recover": 1 1 1 NA 2 1 1 1 NA 1 ...
## $ gender : Factor w/ 3 levels "f","m","unknown": 2 2 1 1 1 1 2 2 2 2 ...
## $ age : num 58 7 11 18 20 9 54 14 39 20 ...
## $ province: Factor w/ 4 levels "Jiangsu","Shanghai",..: 2 2 4 1 1 1 1 3 3 2 ...
## $ Group : Factor w/ 3 levels "Date of onset",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Date : Date, format: "2013-02-19" "2013-02-27" ...
資料視覺化
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 3.4.4
ggplot(data = fluH7N9_china_2013_gather, aes(x = Date, y = age, fill = outcome)) +
stat_density2d(aes(alpha = ..level..), geom = "polygon") +
geom_jitter(aes(color = outcome, shape = gender), size = 1.5) +
geom_rug(aes(color = outcome)) +
labs(
fill = "Outcome",
color = "Outcome",
alpha = "Level",
shape = "Gender",
x = "Date in 2013",
y = "Age",
title = "2013 Influenza A H7N9 cases in China"
)
## Warning: Removed 149 rows containing non-finite values (stat_density2d).
## Warning: Removed 149 rows containing missing values (geom_point).
ggplot(data = fluH7N9_china_2013_gather, aes(x = Date, y = age, fill = outcome)) +
stat_density2d(aes(alpha = ..level..), geom = "polygon") +
geom_jitter(aes(color = outcome, shape = gender), size = 1.5) +
geom_rug(aes(color = outcome)) +
labs(
fill = "Outcome",
color = "Outcome",
alpha = "Level",
shape = "Gender",
x = "Date in 2013",
y = "Age",
title = "2013 Influenza A H7N9 cases in China"
) +
facet_grid(Group ~ province) +
scale_shape_manual(values = c(15, 16, 17)) +
scale_color_brewer(palette="Set1", na.value = "grey50") +
scale_fill_brewer(palette="Set1")
## Warning: Removed 149 rows containing non-finite values (stat_density2d).
## Warning: Removed 149 rows containing missing values (geom_point).
table(fluH7N9_china_2013$outcome, fluH7N9_china_2013$gender)
##
## f m
## Death 9 22
## Recover 12 34
ggplot(data = fluH7N9_china_2013_gather, aes(x = Date, y = age, color = outcome)) +
geom_point(aes(color = outcome, shape = gender), size = 1.5, alpha = 0.6) +
geom_path(aes(group = case_id)) +
facet_wrap( ~ province, ncol = 2) +
scale_shape_manual(values = c(15, 16, 17)) +
scale_color_brewer(palette="Set1", na.value = "grey50") +
scale_fill_brewer(palette="Set1") +
labs(
color = "Outcome",
shape = "Gender",
x = "Date in 2013",
y = "Age",
title = "Time from onset of flu to outcome"
)
## Warning: Removed 149 rows containing missing values (geom_point).
## Warning: Removed 98 rows containing missing values (geom_path).
fluH7N9_china_2013_gather_2 <- fluH7N9_china_2013_gather[, -4] %>% gather(group_2, value, gender:province)
## Warning: attributes are not identical across measure variables;
## they will be dropped
fluH7N9_china_2013_gather_2$value <-
mapvalues(fluH7N9_china_2013_gather_2$value, from = c("m", "f", "unknown","Other"), to = c("Male", "Female", "Unknown gender", "Other province"))
fluH7N9_china_2013_gather_2$value <-
factor(fluH7N9_china_2013_gather_2$value, levels = c("Female", "Male","Unknown gender", "Jiangsu", "Shanghai", "Zhejiang", "Other province"))
str(fluH7N9_china_2013_gather_2)
## 'data.frame': 816 obs. of 6 variables:
## $ case_id: chr "case_1" "case_2" "case_3" "case_4" ...
## $ outcome: Factor w/ 2 levels "Death","Recover": 1 1 1 NA 2 1 1 1 NA 1 ...
## $ Group : Factor w/ 3 levels "Date of onset",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Date : Date, format: "2013-02-19" "2013-02-27" ...
## $ group_2: chr "gender" "gender" "gender" "gender" ...
## $ value : Factor w/ 7 levels "Female","Male",..: 2 2 1 1 1 1 2 2 2 2 ...
p1 <- ggplot(data = fluH7N9_china_2013_gather_2, aes(x = value, fill = outcome, color = outcome)) +
geom_bar(position = "dodge", alpha = 0.7, size = 1) +
scale_fill_brewer(palette="Set1", na.value = "grey50") +
scale_color_brewer(palette="Set1", na.value = "grey50") +
labs(
color = "",
fill = "",
x = "",
y = "Count",
title = "2013 Influenza A H7N9 cases in China")
p1
p2 <- ggplot(data = fluH7N9_china_2013_gather, aes(x = age, fill = outcome, color = outcome)) +
geom_density(alpha = 0.3, size = 1) +
geom_rug() +
scale_color_brewer(palette="Set1", na.value = "grey50") +
scale_fill_brewer(palette="Set1", na.value = "grey50") +
labs(
color = "",
fill = "",
x = "Age",
y = "Density",
title = "Age distribution of flu cases"
)
p2
## Warning: Removed 6 rows containing non-finite values (stat_density).
long to wide or wide to long
library(reshape2)
## Warning: package 'reshape2' was built under R version 3.4.4
##
## Attaching package: 'reshape2'
## The following object is masked from 'package:tidyr':
##
## smiths
data("airquality")
names(airquality) <- tolower(names(airquality))
head(airquality)
## ozone solar.r wind temp month day
## 1 41 190 7.4 67 5 1
## 2 36 118 8.0 72 5 2
## 3 12 149 12.6 74 5 3
## 4 18 313 11.5 62 5 4
## 5 NA NA 14.3 56 5 5
## 6 28 NA 14.9 66 5 6
aqm <- melt(airquality, id=c("month", "day"), na.rm=TRUE)
head(aqm)
## month day variable value
## 1 5 1 ozone 41
## 2 5 2 ozone 36
## 3 5 3 ozone 12
## 4 5 4 ozone 18
## 6 5 6 ozone 28
## 7 5 7 ozone 23
newdata <- dcast(aqm, month ~ variable, mean, margins = c("month", "variable"))
抽取特徵
data(fluH7N9_china_2013)
dataset <- fluH7N9_china_2013
dataset $age[which(dataset$age == "?")] <- NA
dataset$age <- as.numeric(as.character(dataset$age))
dataset$case_id <- paste("case", dataset$case_id, sep = "_")
head(dataset)
## case_id date_of_onset date_of_hospitalisation date_of_outcome outcome
## 1 case_1 2013-02-19 <NA> 2013-03-04 Death
## 2 case_2 2013-02-27 2013-03-03 2013-03-10 Death
## 3 case_3 2013-03-09 2013-03-19 2013-04-09 Death
## 4 case_4 2013-03-19 2013-03-27 <NA> <NA>
## 5 case_5 2013-03-19 2013-03-30 2013-05-15 Recover
## 6 case_6 2013-03-21 2013-03-28 2013-04-26 Death
## gender age province
## 1 m 87 Shanghai
## 2 m 27 Shanghai
## 3 f 35 Anhui
## 4 f 45 Jiangsu
## 5 f 48 Jiangsu
## 6 f 32 Jiangsu
dataset$hospital <- as.factor(ifelse(is.na(dataset$date_of_hospitalisation), 0, 1))
dataset$gender_f <- as.factor(ifelse(dataset$gender == "f", 1, 0))
dataset$province_Jiangsu <- as.factor(ifelse(dataset$province == "Jiangsu", 1, 0))
dataset$province_Shanghai <- as.factor(ifelse(dataset$province == "Shanghai", 1, 0))
dataset$province_Zhejiang <- as.factor(ifelse(dataset$province == "Zhejiang", 1, 0))
dataset$province_other <- as.factor(ifelse(dataset$province == "Zhejiang" | dataset$province
== "Jiangsu" | dataset$province == "Shanghai", 0, 1))
head(dataset)
## case_id date_of_onset date_of_hospitalisation date_of_outcome outcome
## 1 case_1 2013-02-19 <NA> 2013-03-04 Death
## 2 case_2 2013-02-27 2013-03-03 2013-03-10 Death
## 3 case_3 2013-03-09 2013-03-19 2013-04-09 Death
## 4 case_4 2013-03-19 2013-03-27 <NA> <NA>
## 5 case_5 2013-03-19 2013-03-30 2013-05-15 Recover
## 6 case_6 2013-03-21 2013-03-28 2013-04-26 Death
## gender age province hospital gender_f province_Jiangsu province_Shanghai
## 1 m 87 Shanghai 0 0 0 1
## 2 m 27 Shanghai 1 0 0 1
## 3 f 35 Anhui 1 1 0 0
## 4 f 45 Jiangsu 1 1 1 0
## 5 f 48 Jiangsu 1 1 1 0
## 6 f 32 Jiangsu 1 1 1 0
## province_Zhejiang province_other
## 1 0 0
## 2 0 0
## 3 0 1
## 4 0 0
## 5 0 0
## 6 0 0
#as.Date(as.character(dataset$date_of_outcome), format = "%Y-%m-%d") - as.Date(as.character(dataset$date_of_onset), format = "%Y-%m-%d")
dataset$days_onset_to_outcome <- as.numeric(as.character(gsub(" days", "",
as.Date(as.character(dataset$date_of_outcome), format = "%Y-%m-%d") -
as.Date(as.character(dataset$date_of_onset), format = "%Y-%m-%d"))))
dataset$days_onset_to_hospital <- as.numeric(as.character(gsub(" days", "",
as.Date(as.character(dataset$date_of_hospitalisation), format = "%Y-%m-%d") -
as.Date(as.character(dataset$date_of_onset), format = "%Y-%m-%d"))))
summary(dataset$date_of_onset)
## Min. 1st Qu. Median Mean 3rd Qu.
## "2013-02-19" "2013-03-31" "2013-04-08" "2013-04-07" "2013-04-14"
## Max. NA's
## "2013-07-27" "10"
dataset$early_onset <- as.factor(ifelse(dataset$date_of_onset < summary(dataset$date_of_onset)[[3]], 1, 0))
dataset$early_outcome <- as.factor(ifelse(dataset$date_of_outcome < summary(dataset$date_of_outcome)[[3]], 1, 0))
#dataset
dataset <- dataset[,c('case_id','outcome'
, 'age', 'hospital','gender_f'
,'province_Jiangsu'
,'province_Shanghai'
,'province_Zhejiang'
,'province_other'
,'days_onset_to_outcome'
,'days_onset_to_hospital'
,'early_onset'
,'early_outcome')]
head(dataset)
## case_id outcome age hospital gender_f province_Jiangsu province_Shanghai
## 1 case_1 Death 87 0 0 0 1
## 2 case_2 Death 27 1 0 0 1
## 3 case_3 Death 35 1 1 0 0
## 4 case_4 <NA> 45 1 1 1 0
## 5 case_5 Recover 48 1 1 1 0
## 6 case_6 Death 32 1 1 1 0
## province_Zhejiang province_other days_onset_to_outcom